# Modular Object-Oriented Games: A Task Framework for Reinforcement Learning, Psychology, and Neuroscience

arXiv: https://arxiv.org/abs/2102.12616 · HTML: https://mlsanity.com/paper/2102.12616/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 25 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [On the Binding Problem in Artificial Neural Networks](https://mlsanity.com/paper/2012.05208/) (2020)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Building machines that adapt and compute like brains](https://mlsanity.com/paper/1711.04203/) (2017)
- [A Perspective on Objects and Systematic Generalization in Model-Based RL](https://mlsanity.com/paper/1906.01035/) (2019)

## Built on by (most cited first, 4 of 4)

- [From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought](https://mlsanity.com/paper/2306.12672/) (2023)
- [Capturing the objects of vision with neural networks](https://mlsanity.com/paper/2109.03351/) (2021)
- [Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes](https://mlsanity.com/paper/2305.11772/) (2023)
- [Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task](https://mlsanity.com/paper/2212.10367/) (2022)

## Abstract (arXiv)

In recent years, trends towards studying simulated games have gained momentum in the fields of artificial intelligence, cognitive science, psychology, and neuroscience. The intersections of these fields have also grown recently, as researchers increasing study such games using both artificial agents and human or animal subjects. However, implementing games can be a time-consuming endeavor and may require a researcher to grapple with complex codebases that are not easily customized. Furthermore, interdisciplinary researchers studying some combination of artificial intelligence, human psychology, and animal neurophysiology face additional challenges, because existing platforms are designed for only one of these domains. Here we introduce Modular Object-Oriented Games, a Python task framework that is lightweight, flexible, customizable, and designed for use by machine learning, psychology, and neurophysiology researchers.